For long-distance migrations to pay off for individual fish, the energetic costs must be counterbalanced by benefits. Understanding the fitness trade-offs associated with migration is essential for our ability to sustainably manage migratory species. Here, we investigated such trade-offs associated with the spawning migration of Northeast Arctic cod Gadus morhua, a stock of high historical and commercial value known to be able to migrate more than 1000 km southward to reach suitable spawning grounds. Reaching the more distant spawning grounds requires more energy and hence leaves less energy available for processes such as egg production. Previous studies have indicated that increased larval survival (e.g. from rapid early development in warmer southern waters) may offset the parental costs of migration. However, it was suggested that spatial variability in survival of early life stages might cancel out this survival benefit. As an alternative, we ask if the fitness benefit of long-distance migration may reside in increased offspring growth. Using an integral projection model incorporating effects of body length and migration distance, we quantified the increase in offspring growth needed to offset parental costs of long-distance migration on fitness. Our results suggest that a 12.5% length in crease of juvenile cod is required to offset parental costs of long-distance migration. This is within the estimated growth benefit of 20% suggested by drift models of early life stages of cod. These results highlight the potential importance of offspring growth as another factor explaining the benefit of long distance migration, broadening our knowledge on spawning migration.

Body size can have profound impacts on survival, movement, and reproductive schedules shaping individual fitness, making growth a central process in ecological and evolutionary dynamics. Realized growth is the result of a complex interplay between life history schedules, individual variation, and environmental influences. Integrating all of these aspects into growth models is methodologically difficult, depends on the availability of repeated measurements of identifiable individuals, and consequently represents a major challenge in particular for natural populations. Using a unique 30‐yr time series of individual length measurements inferred from scale year rings of wild brown trout, we develop a Bayesian hierarchical model to estimate individual growth trajectories in temporally and spatially varying environments. We reveal a gradual decrease in average juvenile growth, which has carried over to adult life and contributed to decreasing sizes observed at the population level. Commonly studied environmental drivers like temperature and water flow did not explain much of this trend and overall persistent and among‐year individual variation dwarfed temporal variation in growth patterns. Our model and results are relevant to a wide range of questions in ecology and evolution requiring a detailed understanding of growth patterns, including conservation and management of many size‐structured populations.

Population viability analysis is a cornerstone in conservation biology, which depends on reliable predictions of the risk of extinction. A neglected topic in such risk-analyses has been the basic fact that individuals are different, and therefore typically have varying opportunities for survival and reproduction. For instance, in plants with random dispersal the survival of seedlings depend on where they happen to germinate. Moreover, these differences generally persist over time, creating temporal autocorrelation in the vital parameters of each individual. Here we show that ignoring such a fundamental aspect of populations has important consequences for the estimation of key population parameters and extinction risk. We have based our analysis on stochastic matrix and integral equation models with demographic heterogeneity incorporated. Estimates from these models are compared with estimates from models that ignore the heterogeneity and assume that all variation among individuals is merely random. Our results show that estimates of extinction risk may either increase or decrease compared to when heterogeneity is ignored. The direction of the error depends on the type of heterogeneity and on how it is maintained in the population, and the error becomes larger as the heterogeneity becomes more persistent over time. We emphasize that in order to predict extinction risk it is not enough to consider heterogeneity in only some aspects of life history (e.g. survival) without also taking the rest of the life history into account. Our general modeling approach allows us to study various combinations of heterogeneity in survival and reproduction, as well as different mechanisms for maintaining heterogeneity. An important implication of these results is that the majority of current models used in population viability analysis may in many cases underestimate the extinction risk of small, threatened populations.

Individuals in a population often have more‐ or less‐consistent differences in their opportunities for survival and reproduction, as a result of various biological mechanisms. For instance, the survival of a plant seedling can depend on where the seed happens to germinate. An important question is how such individual heterogeneity affects the viability of a population. The expected time to extinction of a population is determined by the amount of demographic stochasticity in the population process, which is influenced by individual heterogeneity. However, whether the heterogeneity increases or decreases the demographic stochasticity has been an unresolved question, except for special cases. We have used a stochastic matrix population model to study the effects of individual heterogeneity on demographic stochasticity. This approach is more general than other methods which have previously been used to study this type of problem. Results/Conclusions We found that individual heterogeneity can increase, decrease, or have no effect on population viability, depending on the heterogeneity itself and on how it is maintained over time. This contrasts with some earlier results which have indicated that heterogeneity should always have a positive effect. Given certain assumptions, these can be shown to be special cases of our model. We can analyze several special cases, such as permanent heterogeneity and source-sink dynamics, as well as cases where individual heterogeneity is a function of age or stage. Thus, we provide a general theoretical framework for studying how individual heterogeneity, created by various biological mechanisms, affects the fluctuations of especially small populations.